Alternatives and similar repositories for mle-interview-questions

Users that are interested in mle-interview-questions are comparing it to the libraries listed below

• jxtngx / lightning-lab
  deep learning with pytorch lightning
  ☆1Updated 7 months ago
• wandb / Groundbreaking-Papers
  ML Research paper summaries, annotated papers and implementation walkthroughs
  ☆114Updated 3 years ago
• eugeneyan / applyingml
  📌 Papers, guides, and mentor interviews on applying machine learning for ApplyingML.com—the ghost knowledge of machine learning.
  ☆200Updated last year
• GokuMohandas / monitoring-ml
  Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.
  ☆86Updated 2 years ago
• chiphuyen / metaflow-transformers-tutorials
  Metaflow tutorials for ODSC West 2021
  ☆64Updated 3 years ago
• the-full-stack / fsdl-text-recognizer-2022
  Source of the FSDL 2022 labs, which are at https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022-labs
  ☆81Updated last year
• abhishekkrthakur / ml_dev_env
  Machine Learning / Deep Learning Environment. Everywhere. Anywhere.
  ☆51Updated 4 years ago
• jacopotagliabue / FREE_7773
  Materials for my 2021 NYU class on NLP and ML Systems (Master of Engineering).
  ☆96Updated 2 years ago
• primaprashant / ml-engineering-blogs
  📚 Curated list of machine learning engineering blogs.
  ☆40Updated last month
• neubig / nlp-from-scratch-assignment-2022
  An assignment for CMU CS11-711 Advanced NLP, building NLP systems from scratch
  ☆171Updated 2 years ago
• msaroufim / ml-design-patterns
  Software Architecture for ML engineers
  ☆408Updated 2 years ago
• nazneenrajani / interpreting-ml-models-course
  Course for Interpreting ML Models
  ☆52Updated 2 years ago
• GokuMohandas / testing-ml
  Learn how to create reliable ML systems by testing code, data and models.
  ☆87Updated 2 years ago
• abhishekkrthakur / chaii-hindi-tamil-question-answering
  chaii: hindi and tamil question answering
  ☆35Updated 3 years ago
• eugeneyan / testing-ml
  🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.
  ☆264Updated 2 years ago
• Machine-Learning-Tokyo / __init__
  ☆138Updated 2 years ago
• init27 / Deep-Learning-Book-Supplementary-Materials
  Supplementary Materials for the Deep Learning Book by Ian Goodfellow et al
  ☆52Updated 2 years ago
• shreyashankar / toy-ml-pipeline
  Toy example of an applied ML pipeline for me to experiment with MLOps tools.
  ☆209Updated 3 years ago
• vopani / fortyone
  Machine Learning begins with Human Learning
  ☆108Updated 3 years ago
• CathyQian / Machine-Learning-System-Design
  A collection of useful resources for Machine Learning System Design
  ☆59Updated 4 years ago
• khuyentran1401 / Machine-learning-pipeline
  Example machine learning pipeline with MLflow and Hydra
  ☆89Updated 2 years ago
• PacktPublishing / Practical-Deep-Learning-at-Scale-with-MLFlow
  Practical Deep Learning at Scale with MLFlow, published by Packt
  ☆160Updated last year
• tmabraham / awesome-fastai
  A curated list of awesome fastai projects/blog posts/tutorials/etc.
  ☆170Updated 3 years ago
• eugeneyan / python-collab-template
  🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.
  ☆147Updated last year
• oke-aditya / pytorch_paper_implementations
  Deep Learning Paper Implementations in PyTorch
  ☆17Updated 2 months ago
• fastai / fastai3
  Research repo for code that may or may not end up in fastai3
  ☆50Updated 3 years ago
• FrancescoSaverioZuppichini / Modern-Python-Doc-Example
  mkdocs + material + cool stuff
  ☆64Updated 2 years ago
• ma7dev / deep-learning-project-template
  A project template where PyTorch Lightning, Pydantic, and more! being used for training MNIST as an example.
  ☆26Updated 2 years ago
• fastai / fastkaggle
  Kaggling for fast kagglers!
  ☆52Updated last year
• RasaHQ / algorithm-whiteboard-resources
  this is where we share notebooks/projects used in your youtube channel
  ☆148Updated 3 years ago